In the last decade, analysis of complex events in videos has attracted significant attention in the computer vision community [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}. Previous research could be categorized as the unsupervised and the supervised methods. Unsupervised methods were typically used for event retrieval [10]}, [7]} where the goal is the retrieval of all related videos semantically relevant to the query video sample. On the other hand, supervised learning has been widely used in event recognition [12]}, [13]} and detection tasks [6]}, [15]} in similar ways to its applications in action recognition [16]}, [17]}, [18]} and generic video classification tasks [19]}, [20]}, [21]}. In the latter case, a classifier is trained on the annotated training set to recognize the event categories of test videos, e.g., the multimedia event detection task of the TRECVID [22]}. In practical applications, it is often desirable to provide explainable results by qualifying the category prediction with the localization of the key pieces of evidence that lead to the recognition decision, which is sometimes referred to as the event recounting.
<FIGURE>